PMID- 37581038 OWN - NLM STAT- PubMed-not-MEDLINE LR - 20230816 IS - 2223-4292 (Print) IS - 2223-4306 (Electronic) IS - 2223-4306 (Linking) VI - 13 IP - 8 DP - 2023 Aug 1 TI - Machine learning prediction model for the rupture status of middle cerebral artery aneurysm in patients with hypertension: a Chinese multicenter study. PG - 4867-4878 LID - 10.21037/qims-22-918 [doi] AB - BACKGROUND: Hypertension is a common comorbidity in patients with unruptured intracranial aneurysms and is closely associated with the rupture of aneurysms. However, only a few studies have focused on the rupture risk of aneurysms comorbid with hypertension. This retrospective study aimed to construct prediction models for the rupture of middle cerebral artery (MCA) aneurysm associated with hypertension using machine learning (ML) algorithms, and the constructed models were externally validated with multicenter datasets. METHODS: We included 322 MCA aneurysm patients comorbid with hypertension who were being treated in four hospitals. All participants underwent computed tomography angiography (CTA), and aneurysm morphological features were measured. Clinical characteristics included sex, age, smoking, and hypertension history. Based on the clinical and morphological characteristics, the training datasets (n=277) were used to fit the ML algorithms to construct prediction models, which were externally validated with the testing datasets (n=45). The prediction performances of the models were assessed by receiver operating characteristic (ROC) curves. RESULTS: The areas under the ROC curve (AUCs) of the k-nearest-neighbor (KNN), neural network (NNet), support vector machine (SVM) and logistic regression (LR) models in the training datasets were 0.83 [95% confidence interval (CI): 0.78-0.88], 0.87 (95% CI: 0.82-0.92), 0.91 (95% CI: 0.88-0.95), and 0.83 (95% CI: 0.77-0.88), respectively, and in the testing datasets were 0.74 (95% CI: 0.59-0.89), 0.82 (95% CI: 0.69-0.94), 0.73 (95% CI: 0.58-0.88), and 0.76 (95% CI: 0.61-0.90), respectively. The aspect ratio (AR) was ranked as the most important variable in the ML models except for NNet. Further analysis showed that the AR had good diagnostic performance, with AUC values of 0.75 in the training datasets and 0.77 in the testing datasets. CONCLUSIONS: The ML models performed reasonably accurately in predicting MCA aneurysm rupture comorbid with hypertension. AR was demonstrated as the leading predictor for the rupture of MCA aneurysm with hypertension. CI - 2023 Quantitative Imaging in Medicine and Surgery. All rights reserved. FAU - Lin, Mengqi AU - Lin M AD - Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. FAU - Xia, Nengzhi AU - Xia N AD - Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. FAU - Lin, Ru AU - Lin R AD - Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. FAU - Xu, Liuhui AU - Xu L AD - Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. FAU - Chen, Yongchun AU - Chen Y AD - Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. FAU - Zhou, Jiafeng AU - Zhou J AD - Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. FAU - Lin, Boli AU - Lin B AD - Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. FAU - Zheng, Kuikui AU - Zheng K AD - Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. FAU - Wang, Hao AU - Wang H AD - Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. FAU - Jia, Xiufen AU - Jia X AD - Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. FAU - Liu, Jinjin AU - Liu J AD - Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. FAU - Zhu, Dongqin AU - Zhu D AD - Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. FAU - Chen, Chao AU - Chen C AD - Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. FAU - Yang, Yunjun AU - Yang Y AD - Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. FAU - Su, Na AU - Su N AD - Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. LA - eng PT - Journal Article DEP - 20230601 PL - China TA - Quant Imaging Med Surg JT - Quantitative imaging in medicine and surgery JID - 101577942 PMC - PMC10423353 OTO - NOTNLM OT - Machine learning (ML) OT - computed tomography angiography (CTA) OT - hypertension OT - intracranial aneurysm OT - middle cerebral artery OT - morphology COIS- Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-22-918/coif). The authors have no conflicts of interest to declare. EDAT- 2023/08/15 06:42 MHDA- 2023/08/15 06:43 PMCR- 2023/08/01 CRDT- 2023/08/15 03:36 PHST- 2022/09/02 00:00 [received] PHST- 2023/05/19 00:00 [accepted] PHST- 2023/08/15 06:43 [medline] PHST- 2023/08/15 06:42 [pubmed] PHST- 2023/08/15 03:36 [entrez] PHST- 2023/08/01 00:00 [pmc-release] AID - qims-13-08-4867 [pii] AID - 10.21037/qims-22-918 [doi] PST - ppublish SO - Quant Imaging Med Surg. 2023 Aug 1;13(8):4867-4878. doi: 10.21037/qims-22-918. Epub 2023 Jun 1.